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Actera.dev VS Aserto

Compare Actera.dev VS Aserto and see what are their differences

Actera.dev

Actera checks every AI agent action against your policies before it runs, then allows it, routes it for human approval or blocks it. Live business context, MCP support and a full audit trail.

Rating
0 reviews
Pricing
Free
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Alternatives Is it good?
Aserto

Fine-grained, scalable authorization in minutes

Rating
0 reviews
Pricing
Freemium
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Alternatives Is it good?
Grapple
Do-It-Yourself Data Analytics & Business Intelligence, Powered by AI sponsored

Which is more popular?

AI Tools popularity
100% vs 0%
alternatives listed
4 vs 27

Base details

Website, pricing, platforms and company facts side by side.

Actera.dev
Aserto
Website actera.dev aserto.com
Pricing
Free
Freemium Official pricing
Platforms
Web
—
Company 1 - 9 employees 2020
Listed in
Security AI Tools Monitoring Tools AI Agents AI Gateway MCP Gateway MCP Servers MCP & AI Agent Tooling MCP AI Governance Governance AI +9
Developer Tools Security APIs Productivity Project Management +2

About Actera.dev and Aserto

In their own words, as submitted to SaaSHub.

Actera.dev
Aserto

Actera is a guardrail layer for AI agents. Agents send each action to Actera before it runs, such as a refund, a payment, a data export or a production deploy. Actera checks it against your policies and live context (approvals already given, workflow state, spend so far, what other agents have...

Read more about Actera.dev

Aserto helps you build secure applications. It makes it easy to add fine-grained, policy-based, real-time access control to your applications and APIs. Aserto handles all the heavy lifting required to achieve secure, scalable, high-performance access management. It offers blazing-fast...

Read more about Aserto

Features and specs

What each product offers, as listed by its team.

Actera.dev 5 features
Aserto 6 features
  • Policy decisions
    Allow, require approval or block each agent action before it runs
  • Human approval
    Routes actions to the right approver with full context; the action resumes once approved
  • Context-aware rules
    Policies use prior approvals, workflow state, spend so far and other agents' actions
  • Audit trail
    Every decision is recorded with the policy behind it
  • Integrations
    Claude Code and Codex plugins, TypeScript SDK, remote MCP connector for Claude
  • Graph directory
    Built in graph-based directory
  • OPA decision engine
    Support for OPA policies
  • ReBAC
    Supports relationship based access control
  • ABAC
    Supports attribute-based access control
  • RBAC
    Support role-based access control
  • PBAM
    Supports policy-based access management

Videos

Walkthroughs and reviews on video.

Actera.dev 0 videos + Add
Aserto 1 video + Add

No Actera.dev videos yet. You could help us improve this page by suggesting one.

Intro to modern authorization

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
Actera.dev
Aserto
100% 100%
AI Tools
0% 0%
0% 0%
Developer Tools
100% 100%
20% 20%
Security
80% 80%
100% 100%
Monitoring Tools
0% 0%

Questions & Answers

As answered by people managing Actera.dev and Aserto.

What makes your product unique?

Actera.dev's answer

Actera checks each action an AI agent tries to take before it runs, not after. Every action gets one of three outcomes: allowed, sent for approval, or blocked. Decisions use live context, such as approvals already given, the state of the workflow, spend so far today and what other agents have done, so one policy can give different answers to the same request. Policies are enforced outside the model, so no prompt, model update or clever wording can change them.

Why should a person choose your product over its competitors?

Actera.dev's answer

Authorization tools like Cerbos and Permit.io answer whether a user may access a resource. Actera is built for agent actions: it adds approval routing with full context, actions that resume once approved, and a decision record with the policy behind every attempt. It also ships ready-made enforcement for coding agents (open-source plugins for Claude Code and Codex), a TypeScript SDK for your own agents, and a remote MCP connector so policies can be reviewed and written from Claude. Teams can test rules on real scenarios before publishing them, and there is a free plan.

How would you describe the primary audience of your product?

Actera.dev's answer

Companies putting AI agents to work on real systems: engineering teams running coding agents such as Claude Code and Codex, and product teams building agents that issue refunds, change records, export data or deploy code. Finance, engineering and operations leads use Actera to set what each agent can do on its own, what needs approval and what is off limits, and compliance teams use the decision record to show auditors why an agent acted.

Which are the primary technologies used for building your product?

Actera.dev's answer

Go for the API and policy engine, with gRPC and protobuf contracts, PostgreSQL and Redis. The web app is React and TypeScript. It runs on Kubernetes on AWS. Integrations include a TypeScript SDK, plugins for Claude Code and Codex built on their hook systems, and a remote MCP server for Claude.

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